Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks
Authors: 
Ruifeng Liu, Qu Liu, Tingjian GeAuthors Info & Claims
Ruifeng Liu, Qu Liu, Tingjian GeAuthors Info & ClaimsPages 1512 - 1523
Abstract
We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. Our architecture includes any dynamic graph neural network model as the back end applied over the network data, and per CAT front end models that return results with their confidence to users. We devise a data filtering algorithm that feeds a provably optimal subset of data in the embedding space from back end model to front end models. Secondly, to ensure fairness in terms of query result accuracy for different CATs and users, we propose a fairness metric and a fairness-aware training scheduling algorithm, along with accuracy guarantees on fairness estimation. Our experiments over five real-world datasets show that our proposed solution is effective, efficient, fair, extensible, and adaptive.
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Index Terms
- Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks
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Information & Contributors
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Published In
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
- Ambuj Singh,
- Yizhou Sun,
- Program Chairs:
- Leman Akoglu,
- Dimitrios Gunopulos,
- Xifeng Yan,
- Ravi Kumar,
- Fatma Ozcan,
- Jieping Ye
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Published: 04 August 2023
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- New England Transportation Consortium
- NSF (National Science Foundation)
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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 6 - 10, 2023
CA, Long Beach, USA
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